Real-time semantic segmentation is the task of achieving computationally efficient semantic segmentation (while maintaining a base level of accuracy).
( Image credit: TorchSeg )
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BiSeNet has been proved to be a popular two-stream network for real-time segmentation.
Ranked #2 on Real-Time Semantic Segmentation on Cityscapes test
To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices.
Ranked #1 on Medical Image Segmentation on KvasirCapsule-SEG
We introduce a lightweight unit, conditional channel weighting, to replace costly pointwise (1x1) convolutions in shuffle blocks.
Ranked #11 on Pose Estimation on COCO test-dev
Based on the CFP module, we built CFPNet for real-time semantic segmentation which applied a series of dilated convolution channels to extract effective features.
Our method achieves new state-of-the-art trade-off between accuracy and speed on both Cityscapes and CamVid dataset.
Ranked #1 on Real-Time Semantic Segmentation on Cityscapes test
Colonoscopy is the gold standard for examination and detection of colorectal polyps.
We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder.
Ranked #2 on Real-Time Semantic Segmentation on CamVid
Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks.
Ranked #5 on Medical Image Segmentation on CVC-ClinicDB
The proposed architecture relies on our fast spatial attention, which is a simple yet efficient modification of the popular self-attention mechanism and captures the same rich spatial context at a small fraction of the computational cost, by changing the order of operations.
We propose to treat these spatial details and categorical semantics separately to achieve high accuracy and high efficiency for realtime semantic segmentation.
Ranked #1 on Real-Time Semantic Segmentation on COCO-Stuff